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- W2348893123 abstract "Particle swarm optimization (PSO) was modified by variation method of particle velocity, and a variation PSO (VPSO) algorithm was proposed to overcome the shortcomings of PSO, such as premature convergence and local optimization. The VPSO algorithm is combined with Elman neural network (ENN) to form a VPSO-ENN hybrid algorithm. Compared with the hybrid algorithm of genetic algorithm (GA) and BP neural network (GA-BP), VPSO-ENN has less adjustable parameters, faster convergence speed and higher identification precision in the numerical experiment. A system for identifying logging parameters was established based on VPSO-ENN. The results of an engineering case indicate that the intelligent identification system is effective in the lithology identification." @default.
- W2348893123 created "2016-06-24" @default.
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- W2348893123 date "2008-01-01" @default.
- W2348893123 modified "2023-09-25" @default.
- W2348893123 title "Adaptive Identification of Logging Lithology Based on VPSO-ENN Hybrid Algorithm" @default.
- W2348893123 hasPublicationYear "2008" @default.
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